Limit Theorems for Simulation-based Optimization via

This paper develops fundamental theory related to the use of simulation-based non-adaptive random
search as a means of optimizing a function that can be expressed as an expectation. Our
results establish rates of convergence that express the trade-off between exploration and estimation,
and fully characterize the limit distributions that arise. Our rates of convergence results
should be viewed as a baseline against which to compare more intelligent algorithms.